REPOGEO REPORT · LITE

Thinklab-SJTU/Awesome-LLM4AD

Default branch main · commit df33d2af · scanned 6/28/2026, 8:13:22 AM

GitHub: 1,859 stars · 109 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Thinklab-SJTU/Awesome-LLM4AD, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

hightopics#1

Add 'autonomous-driving' and 'llm4ad' to topics

Why:

CURRENT

large-language-models, vision-language-action-model, vision-language-model, world-model

COPY-PASTE FIX

large-language-models, vision-language-action-model, vision-language-model, world-model, autonomous-driving, llm4ad, awesome-list, survey

highreadme#2

Strengthen README's opening to clarify repo type and domain

Why:

CURRENT

This is a collection of research papers about **LLM-for-Autonomous-Driving(LLM4AD)**. The repository will be continuously updated to track the frontier of LLM4AD (Large Language Models for Autonomous Driving), which encompasses VLM4AD (Vision-Language Models for AD) and VLA4AD (Vision-Language-Action models for AD) as integral components of this unified paradigm.

COPY-PASTE FIX

This repository serves as a comprehensive, continually updated **curated list and survey of research papers and resources** focused on **Large Language Models for Autonomous Driving (LLM4AD)**. It encompasses VLM4AD (Vision-Language Models for AD) and VLA4AD (Vision-Language-Action models for AD) as integral components of this unified paradigm, tracking the frontier of this rapidly evolving field.

mediumhomepage#3
Add homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/2311.01043
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface Thinklab-SJTU/Awesome-LLM4AD

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ChatGPT

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

ChatGPT · recommended 2×
Bard · recommended 2×
Llama 2 · recommended 2×
GPT-4 · recommended 2×
Claude 2 · recommended 2×

CATEGORY QUERY
How can large language models be applied to autonomous driving systems for improved intelligence?
you: not recommended
AI recommended (in order):
1. ChatGPT
2. Bard
3. Llama 2
4. GPT-4
5. Claude 2
6. PaLM 2
7. Llama 2
8. Falcon
9. ChatGPT
10. Bard
11. GPT-4
12. Claude 2
13. GitHub Copilot
14. Code Llama
AI recommended 14 alternatives but never named Thinklab-SJTU/Awesome-LLM4AD. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the cutting-edge vision-language-action models for developing self-driving car capabilities?
you: not recommended
AI recommended (in order):
1. LINGO-1
2. DriveGPT
3. Tesla's Autopilot/FSD
4. Waymo Driver
5. Mobileye's REM
6. NVIDIA DRIVE
AI recommended 6 alternatives but never named Thinklab-SJTU/Awesome-LLM4AD. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of Thinklab-SJTU/Awesome-LLM4AD?
pass
AI did not name Thinklab-SJTU/Awesome-LLM4AD — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Thinklab-SJTU/Awesome-LLM4AD in production, what risks or prerequisites should they evaluate first?
pass
AI named Thinklab-SJTU/Awesome-LLM4AD explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo Thinklab-SJTU/Awesome-LLM4AD solve, and who is the primary audience?
pass
AI did not name Thinklab-SJTU/Awesome-LLM4AD — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

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Thinklab-SJTU/Awesome-LLM4AD — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite